
"""simple docstring"""






import argparse
import json
from pathlib import Path

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import YolosConfig, YolosForObjectDetection, YolosImageProcessor
from transformers.utils import logging


logging.set_verbosity_info()
UpperCAmelCase        =				logging.get_logger(__name__)




def __magic_name__	(				_lowerCamelCase:		str			)					->						YolosConfig:

	'''simple docstring'''




	lowerCAmelCase												=  YolosConfig()

	# size of the architecture
	if "yolos_ti" in yolos_name:
		lowerCAmelCase												=  192
		lowerCAmelCase												=  768
		lowerCAmelCase												=  12
		lowerCAmelCase												=  3
		lowerCAmelCase												=  [800, 1_333]
		lowerCAmelCase												=  False
	elif yolos_name == "yolos_s_dWr":
		lowerCAmelCase												=  330
		lowerCAmelCase												=  14
		lowerCAmelCase												=  6
		lowerCAmelCase												=  1_320
	elif "yolos_s" in yolos_name:
		lowerCAmelCase												=  384
		lowerCAmelCase												=  1_536
		lowerCAmelCase												=  12
		lowerCAmelCase												=  6
	elif "yolos_b" in yolos_name:
		lowerCAmelCase												=  [800, 1_344]

	lowerCAmelCase												=  91
	lowerCAmelCase												=  '''huggingface/label-files'''
	lowerCAmelCase												=  '''coco-detection-id2label.json'''
	lowerCAmelCase												=  json.load(open(hf_hub_download(_lowerCamelCase,					_lowerCamelCase,					repo_type='''dataset'''			),					'''r'''			)			)
	lowerCAmelCase												=  {int(_lowerCamelCase			): v for k, v in idalabel.items()}
	lowerCAmelCase												=  idalabel
	lowerCAmelCase												=  {v: k for k, v in idalabel.items()}

	return config




def __magic_name__	(				_lowerCamelCase:		dict,					_lowerCamelCase:		YolosConfig,					_lowerCamelCase:		bool = False			)					->						int:

	'''simple docstring'''




	for i in range(config.num_hidden_layers			):
		# read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
		lowerCAmelCase												=  state_dict.pop(F"""blocks.{i}.attn.qkv.weight"""			)
		lowerCAmelCase												=  state_dict.pop(F"""blocks.{i}.attn.qkv.bias"""			)
		# next, add query, keys and values (in that order) to the state dict
		lowerCAmelCase												=  in_proj_weight[: config.hidden_size, :]
		lowerCAmelCase												=  in_proj_bias[: config.hidden_size]
		lowerCAmelCase												=  in_proj_weight[
		    config.hidden_size : config.hidden_size * 2, :
		]
		lowerCAmelCase												=  in_proj_bias[
		    config.hidden_size : config.hidden_size * 2
		]
		lowerCAmelCase												=  in_proj_weight[-config.hidden_size :, :]
		lowerCAmelCase												=  in_proj_bias[-config.hidden_size :]




def __magic_name__	(				_lowerCamelCase:		str			)					->						str:

	'''simple docstring'''




	if "backbone" in name:
		lowerCAmelCase												=  name.replace('''backbone''',					'''vit'''			)
	if "cls_token" in name:
		lowerCAmelCase												=  name.replace('''cls_token''',					'''embeddings.cls_token'''			)
	if "det_token" in name:
		lowerCAmelCase												=  name.replace('''det_token''',					'''embeddings.detection_tokens'''			)
	if "mid_pos_embed" in name:
		lowerCAmelCase												=  name.replace('''mid_pos_embed''',					'''encoder.mid_position_embeddings'''			)
	if "pos_embed" in name:
		lowerCAmelCase												=  name.replace('''pos_embed''',					'''embeddings.position_embeddings'''			)
	if "patch_embed.proj" in name:
		lowerCAmelCase												=  name.replace('''patch_embed.proj''',					'''embeddings.patch_embeddings.projection'''			)
	if "blocks" in name:
		lowerCAmelCase												=  name.replace('''blocks''',					'''encoder.layer'''			)
	if "attn.proj" in name:
		lowerCAmelCase												=  name.replace('''attn.proj''',					'''attention.output.dense'''			)
	if "attn" in name:
		lowerCAmelCase												=  name.replace('''attn''',					'''attention.self'''			)
	if "norm1" in name:
		lowerCAmelCase												=  name.replace('''norm1''',					'''layernorm_before'''			)
	if "norm2" in name:
		lowerCAmelCase												=  name.replace('''norm2''',					'''layernorm_after'''			)
	if "mlp.fc1" in name:
		lowerCAmelCase												=  name.replace('''mlp.fc1''',					'''intermediate.dense'''			)
	if "mlp.fc2" in name:
		lowerCAmelCase												=  name.replace('''mlp.fc2''',					'''output.dense'''			)
	if "class_embed" in name:
		lowerCAmelCase												=  name.replace('''class_embed''',					'''class_labels_classifier'''			)
	if "bbox_embed" in name:
		lowerCAmelCase												=  name.replace('''bbox_embed''',					'''bbox_predictor'''			)
	if "vit.norm" in name:
		lowerCAmelCase												=  name.replace('''vit.norm''',					'''vit.layernorm'''			)

	return name




def __magic_name__	(				_lowerCamelCase:		dict,					_lowerCamelCase:		YolosForObjectDetection			)					->						dict:

	'''simple docstring'''




	for key in orig_state_dict.copy().keys():
		lowerCAmelCase												=  orig_state_dict.pop(_lowerCamelCase			)

		if "qkv" in key:
			lowerCAmelCase												=  key.split('''.'''			)
			lowerCAmelCase												=  int(key_split[2]			)
			lowerCAmelCase												=  model.vit.encoder.layer[layer_num].attention.attention.all_head_size
			if "weight" in key:
				lowerCAmelCase												=  val[:dim, :]
				lowerCAmelCase												=  val[
				    dim : dim * 2, :
				]
				lowerCAmelCase												=  val[-dim:, :]
			else:
				lowerCAmelCase												=  val[:dim]
				lowerCAmelCase												=  val[dim : dim * 2]
				lowerCAmelCase												=  val[-dim:]
		else:
			lowerCAmelCase												=  val

	return orig_state_dict




def __magic_name__	(				)					->						torch.Tensor:

	'''simple docstring'''




	lowerCAmelCase												=  '''http://images.cocodataset.org/val2017/000000039769.jpg'''
	lowerCAmelCase												=  Image.open(requests.get(_lowerCamelCase,					stream=_lowerCamelCase			).raw			)
	return im




@torch.no_grad()
def __magic_name__	(				_lowerCamelCase:		str,					_lowerCamelCase:		str,					_lowerCamelCase:		str,					_lowerCamelCase:		bool = False			)					->						Optional[Any]:

	'''simple docstring'''




	lowerCAmelCase												=  get_yolos_config(_lowerCamelCase			)

	# load original state_dict
	lowerCAmelCase												=  torch.load(_lowerCamelCase,					map_location='''cpu'''			)['''model''']

	# load 🤗 model
	lowerCAmelCase												=  YolosForObjectDetection(_lowerCamelCase			)
	model.eval()
	lowerCAmelCase												=  convert_state_dict(_lowerCamelCase,					_lowerCamelCase			)
	model.load_state_dict(_lowerCamelCase			)

	# Check outputs on an image, prepared by YolosImageProcessor
	lowerCAmelCase												=  800 if yolos_name != '''yolos_ti''' else 512
	lowerCAmelCase												=  YolosImageProcessor(format='''coco_detection''',					size=_lowerCamelCase			)
	lowerCAmelCase												=  image_processor(images=prepare_img(),					return_tensors='''pt'''			)
	lowerCAmelCase												=  model(**_lowerCamelCase			)
	lowerCAmelCase							,       lowerCAmelCase												=  outputs.logits, outputs.pred_boxes

	lowerCAmelCase							,       lowerCAmelCase												=  None, None
	if yolos_name == "yolos_ti":
		lowerCAmelCase												=  torch.tensor(
		    [[-39.5022, -11.9820, -17.6888], [-29.9574, -9.9769, -17.7691], [-42.3281, -20.7200, -30.6294]]			)
		lowerCAmelCase												=  torch.tensor(
		    [[0.4021, 0.0836, 0.7979], [0.0184, 0.2609, 0.0364], [0.1781, 0.2004, 0.2095]]			)
	elif yolos_name == "yolos_s_200_pre":
		lowerCAmelCase												=  torch.tensor(
		    [[-24.0248, -10.3024, -14.8290], [-42.0392, -16.8200, -27.4334], [-27.2743, -11.8154, -18.7148]]			)
		lowerCAmelCase												=  torch.tensor(
		    [[0.2559, 0.5455, 0.4706], [0.2989, 0.7279, 0.1875], [0.7732, 0.4017, 0.4462]]			)
	elif yolos_name == "yolos_s_300_pre":
		lowerCAmelCase												=  torch.tensor(
		    [[-36.2220, -14.4385, -23.5457], [-35.6970, -14.7583, -21.3935], [-31.5939, -13.6042, -16.8049]]			)
		lowerCAmelCase												=  torch.tensor(
		    [[0.7614, 0.2316, 0.4728], [0.7168, 0.4495, 0.3855], [0.4996, 0.1466, 0.9996]]			)
	elif yolos_name == "yolos_s_dWr":
		lowerCAmelCase												=  torch.tensor(
		    [[-42.8668, -24.1049, -41.1690], [-34.7456, -14.1274, -24.9194], [-33.7898, -12.1946, -25.6495]]			)
		lowerCAmelCase												=  torch.tensor(
		    [[0.5587, 0.2773, 0.0605], [0.5004, 0.3014, 0.9994], [0.4999, 0.1548, 0.9994]]			)
	elif yolos_name == "yolos_base":
		lowerCAmelCase												=  torch.tensor(
		    [[-40.6064, -24.3084, -32.6447], [-55.1990, -30.7719, -35.5877], [-51.4311, -33.3507, -35.6462]]			)
		lowerCAmelCase												=  torch.tensor(
		    [[0.5555, 0.2794, 0.0655], [0.9049, 0.2664, 0.1894], [0.9183, 0.1984, 0.1635]]			)
	else:
		raise ValueError(F"""Unknown yolos_name: {yolos_name}"""			)

	assert torch.allclose(logits[0, :3, :3],					_lowerCamelCase,					atol=1E-4			)
	assert torch.allclose(pred_boxes[0, :3, :3],					_lowerCamelCase,					atol=1E-4			)

	Path(_lowerCamelCase			).mkdir(exist_ok=_lowerCamelCase			)
	print(F"""Saving model {yolos_name} to {pytorch_dump_folder_path}"""			)
	model.save_pretrained(_lowerCamelCase			)
	print(F"""Saving image processor to {pytorch_dump_folder_path}"""			)
	image_processor.save_pretrained(_lowerCamelCase			)

	if push_to_hub:
		lowerCAmelCase												=  {
		    '''yolos_ti''': '''yolos-tiny''',
		    '''yolos_s_200_pre''': '''yolos-small''',
		    '''yolos_s_300_pre''': '''yolos-small-300''',
		    '''yolos_s_dWr''': '''yolos-small-dwr''',
		    '''yolos_base''': '''yolos-base''',
		}

		print('''Pushing to the hub...'''			)
		lowerCAmelCase												=  model_mapping[yolos_name]
		image_processor.push_to_hub(_lowerCamelCase,					organization='''hustvl'''			)
		model.push_to_hub(_lowerCamelCase,					organization='''hustvl'''			)


if __name__ == "__main__":
					UpperCAmelCase        =				argparse.ArgumentParser()
					# Required parameters
					parser.add_argument(
					    """--yolos_name""",
					    default="""yolos_s_200_pre""",
					    type=str,
					    help=(
					        """Name of the YOLOS model you'd like to convert. Should be one of 'yolos_ti', 'yolos_s_200_pre',"""
					        """ 'yolos_s_300_pre', 'yolos_s_dWr', 'yolos_base'."""
					    ),
					)
					parser.add_argument(
					    """--checkpoint_path""", default=None, type=str, help="""Path to the original state dict (.pth file)."""
					)
					parser.add_argument(
					    """--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model directory."""
					)
					parser.add_argument(
					    """--push_to_hub""", action="""store_true""", help="""Whether or not to push the converted model to the 🤗 hub."""
					)

					UpperCAmelCase        =				parser.parse_args()
					convert_yolos_checkpoint(args.yolos_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub)




"""simple docstring"""






from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_torch_available,
)


UpperCAmelCase        =				{
    """configuration_roberta_prelayernorm""": [
        """ROBERTA_PRELAYERNORM_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """RobertaPreLayerNormConfig""",
        """RobertaPreLayerNormOnnxConfig""",
    ],
}

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					UpperCAmelCase        =				[
					    """ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST""",
					    """RobertaPreLayerNormForCausalLM""",
					    """RobertaPreLayerNormForMaskedLM""",
					    """RobertaPreLayerNormForMultipleChoice""",
					    """RobertaPreLayerNormForQuestionAnswering""",
					    """RobertaPreLayerNormForSequenceClassification""",
					    """RobertaPreLayerNormForTokenClassification""",
					    """RobertaPreLayerNormModel""",
					    """RobertaPreLayerNormPreTrainedModel""",
					]

try:
					if not is_tf_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					UpperCAmelCase        =				[
					    """TF_ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST""",
					    """TFRobertaPreLayerNormForCausalLM""",
					    """TFRobertaPreLayerNormForMaskedLM""",
					    """TFRobertaPreLayerNormForMultipleChoice""",
					    """TFRobertaPreLayerNormForQuestionAnswering""",
					    """TFRobertaPreLayerNormForSequenceClassification""",
					    """TFRobertaPreLayerNormForTokenClassification""",
					    """TFRobertaPreLayerNormMainLayer""",
					    """TFRobertaPreLayerNormModel""",
					    """TFRobertaPreLayerNormPreTrainedModel""",
					]

try:
					if not is_flax_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					UpperCAmelCase        =				[
					    """FlaxRobertaPreLayerNormForCausalLM""",
					    """FlaxRobertaPreLayerNormForMaskedLM""",
					    """FlaxRobertaPreLayerNormForMultipleChoice""",
					    """FlaxRobertaPreLayerNormForQuestionAnswering""",
					    """FlaxRobertaPreLayerNormForSequenceClassification""",
					    """FlaxRobertaPreLayerNormForTokenClassification""",
					    """FlaxRobertaPreLayerNormModel""",
					    """FlaxRobertaPreLayerNormPreTrainedModel""",
					]


if TYPE_CHECKING:
					from .configuration_roberta_prelayernorm import (
					    ROBERTA_PRELAYERNORM_PRETRAINED_CONFIG_ARCHIVE_MAP,
					    RobertaPreLayerNormConfig,
					    RobertaPreLayerNormOnnxConfig,
					)

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_roberta_prelayernorm import (
										    ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST,
										    RobertaPreLayerNormForCausalLM,
										    RobertaPreLayerNormForMaskedLM,
										    RobertaPreLayerNormForMultipleChoice,
										    RobertaPreLayerNormForQuestionAnswering,
										    RobertaPreLayerNormForSequenceClassification,
										    RobertaPreLayerNormForTokenClassification,
										    RobertaPreLayerNormModel,
										    RobertaPreLayerNormPreTrainedModel,
										)

					try:
										if not is_tf_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_tf_roberta_prelayernorm import (
										    TF_ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST,
										    TFRobertaPreLayerNormForCausalLM,
										    TFRobertaPreLayerNormForMaskedLM,
										    TFRobertaPreLayerNormForMultipleChoice,
										    TFRobertaPreLayerNormForQuestionAnswering,
										    TFRobertaPreLayerNormForSequenceClassification,
										    TFRobertaPreLayerNormForTokenClassification,
										    TFRobertaPreLayerNormMainLayer,
										    TFRobertaPreLayerNormModel,
										    TFRobertaPreLayerNormPreTrainedModel,
										)

					try:
										if not is_flax_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_flax_roberta_prelayernorm import (
										    FlaxRobertaPreLayerNormForCausalLM,
										    FlaxRobertaPreLayerNormForMaskedLM,
										    FlaxRobertaPreLayerNormForMultipleChoice,
										    FlaxRobertaPreLayerNormForQuestionAnswering,
										    FlaxRobertaPreLayerNormForSequenceClassification,
										    FlaxRobertaPreLayerNormForTokenClassification,
										    FlaxRobertaPreLayerNormModel,
										    FlaxRobertaPreLayerNormPreTrainedModel,
										)

else:
					import sys

					UpperCAmelCase        =				_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)


